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Record W20019925 · doi:10.1093/pch/8.2.93

Thermochemical Conversion of Oil sand Bitumen in Delayed Coking Reactor

2008· article· en· W20019925 on OpenAlexaboutno aff
See Hoon Lee, Sang Jun Yoon, Jae-Goo Lee, Jae‐Ho Kim

Bibliographic record

VenueJournal of the Korean Institute of Resources Recycling · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsAsphaltCokeDelayed cokerDiesel fuelPetroleum cokeYield (engineering)Coker unitWaste managementFuel oilEnvironmental scienceMaterials sciencePulp and paper industryPetroleum engineeringMetallurgyGeologyComposite materialEngineering

Abstract

fetched live from OpenAlex

The study of coking technology to upgrade oil sand bitumen which is considered as alternative fuel was performed by using thermogravity analyzer and delayed coking reactor(600ml). To analyzed and compared coking characteristics of oil sand bitumen, the reactivities of oil sand bitumen were measured in the TGA. At the temperature conditions of and the temperature rising velocity of . the termination time of coking reaction and conversion efficiencies increased with an increase of bed temperature. However the increase rate decreased over . So the coking reaction with oil sand bitumen might be over . Also the termination time decreased with increasing the temperature rising velocity. But the content of coke increased with increasing temperature rising velocity. At the experiments in the delayed coker, the temperature condition at maximum oil yield was and the fuel properties of oil from coking reaction was almost equal with conventional diesel. It was verified that the coking process might be useful process to upgrade the oil sand bitumem by using API and SIMDAS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.229
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Korean Institute of Resources RecyclingSame topicPetroleum Processing and AnalysisFrench-language works237,207